3D SA-UNet: 3D Spatial Attention UNet with 3D Atrous Spatial Pyramid Pooling for White Matter Hyperintensities Segmentation

TL;DR

This paper introduces 3D SA-UNet, a deep learning model that combines 3D spatial attention and atrous spatial pyramid pooling to improve white matter hyperintensities segmentation in MRI scans, addressing challenges of small lesion detection.

Contribution

The paper presents a novel 3D deep learning architecture integrating spatial attention and multi-scale feature extraction for WMH segmentation, outperforming existing models.

Findings

Higher segmentation accuracy than state-of-the-art methods

Effective highlighting of lesion features via spatial attention

Enhanced multi-scale feature capture with 3D ASPP

Abstract

White Matter Hyperintensity (WMH) is an imaging feature related to various diseases such as dementia and stroke. Accurately segmenting WMH using computer technology is crucial for early disease diagnosis. However, this task remains challenging due to the small lesions with low contrast and high discontinuity in the images, which contain limited contextual and spatial information. To address this challenge, we propose a deep learning model called 3D Spatial Attention U-Net (3D SA-UNet) for automatic WMH segmentation using only Fluid Attenuation Inversion Recovery (FLAIR) scans. The 3D SA-UNet introduces a 3D Spatial Attention Module that highlights important lesion features, such as WMH, while suppressing unimportant regions. Additionally, to capture features at different scales, we extend the Atrous Spatial Pyramid Pooling (ASPP) module to a 3D version, enhancing the segmentation performance of the network. We evaluate our method on publicly available dataset and demonstrate the effectiveness of 3D spatial attention module and 3D ASPP in WMH segmentation. Through experimental results, it has been demonstrated that our proposed 3D SA-UNet model achieves higher accuracy compared to other state-of-the-art 3D convolutional neural networks.